from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
		import datasets




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			@slow
			@require_torch
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							__lowercase					     =				EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''' ,  '''prajjwal1/bert-tiny'''		)
							__lowercase					     =				BertTokenizer.from_pretrained('''bert-base-uncased'''		)

							__lowercase					     =				bertabert.config.encoder.vocab_size
							__lowercase					     =				tokenizer.sep_token_id
							__lowercase					     =				tokenizer.cls_token_id
							__lowercase					     =				1_28

							__lowercase					     =				datasets.load_dataset('''cnn_dailymail''' ,  '''3.0.0''' ,  split='''train[:1%]'''		)
							__lowercase					     =				datasets.load_dataset('''cnn_dailymail''' ,  '''3.0.0''' ,  split='''validation[:1%]'''		)

							__lowercase					     =				train_dataset.select(range(32		)		)
							__lowercase					     =				val_dataset.select(range(16		)		)

							__lowercase					     =				4

							def _map_to_encoder_decoder_inputs(lowerCAmelCase__		):
											# Tokenizer will automatically set [BOS] <text> [EOS]
											__lowercase					     =				tokenizer(batch['''article'''] ,  padding='''max_length''' ,  truncation=lowerCAmelCase__ ,  max_length=5_12		)
											__lowercase					     =				tokenizer(batch['''highlights'''] ,  padding='''max_length''' ,  truncation=lowerCAmelCase__ ,  max_length=1_28		)
											__lowercase					     =				inputs.input_ids
											__lowercase					     =				inputs.attention_mask

											__lowercase					     =				outputs.input_ids
											__lowercase					     =				outputs.input_ids.copy()
											__lowercase					     =				[
											    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
											]
											__lowercase					     =				outputs.attention_mask

											assert all(len(lowerCAmelCase__		) == 5_12 for x in inputs.input_ids		)
											assert all(len(lowerCAmelCase__		) == 1_28 for x in outputs.input_ids		)

											return batch

							def _compute_metrics(lowerCAmelCase__		):
											__lowercase					     =				pred.label_ids
											__lowercase					     =				pred.predictions

											# all unnecessary tokens are removed
											__lowercase					     =				tokenizer.batch_decode(lowerCAmelCase__ ,  skip_special_tokens=lowerCAmelCase__		)
											__lowercase					     =				tokenizer.batch_decode(lowerCAmelCase__ ,  skip_special_tokens=lowerCAmelCase__		)

											__lowercase					     =				sum([int(pred_str[i] == label_str[i]		) for i in range(len(lowerCAmelCase__		)		)]		) / len(lowerCAmelCase__		)

											return {"accuracy": accuracy}

							# map train dataset
							__lowercase					     =				train_dataset.map(
							    _map_to_encoder_decoder_inputs ,  batched=lowerCAmelCase__ ,  batch_size=lowerCAmelCase__ ,  remove_columns=['''article''', '''highlights'''] ,  )
							train_dataset.set_format(
							    type='''torch''' ,  columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] ,  )

							# same for validation dataset
							__lowercase					     =				val_dataset.map(
							    _map_to_encoder_decoder_inputs ,  batched=lowerCAmelCase__ ,  batch_size=lowerCAmelCase__ ,  remove_columns=['''article''', '''highlights'''] ,  )
							val_dataset.set_format(
							    type='''torch''' ,  columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] ,  )

							__lowercase					     =				self.get_auto_remove_tmp_dir()

							__lowercase					     =				SeqaSeqTrainingArguments(
							    output_dir=lowerCAmelCase__ ,  per_device_train_batch_size=lowerCAmelCase__ ,  per_device_eval_batch_size=lowerCAmelCase__ ,  predict_with_generate=lowerCAmelCase__ ,  evaluation_strategy='''steps''' ,  do_train=lowerCAmelCase__ ,  do_eval=lowerCAmelCase__ ,  warmup_steps=0 ,  eval_steps=2 ,  logging_steps=2 ,  )

							# instantiate trainer
							__lowercase					     =				SeqaSeqTrainer(
							    model=lowerCAmelCase__ ,  args=lowerCAmelCase__ ,  compute_metrics=_compute_metrics ,  train_dataset=lowerCAmelCase__ ,  eval_dataset=lowerCAmelCase__ ,  tokenizer=lowerCAmelCase__ ,  )

							# start training
							trainer.train()
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a    :	int         =    logging.get_logger(__name__)

__a    :	Optional[int]         =    {
    """google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Any     					=		'''pegasus'''
			__a    :					Optional[Any]     					=		['''past_key_values''']
			__a    :					Tuple     					=		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





			def __init__(  self ,  lowerCAmelCase__=5_02_65 ,  lowerCAmelCase__=10_24 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=40_96 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=40_96 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=True ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=10_24 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.02 ,  lowerCAmelCase__=0 ,  lowerCAmelCase__=False ,  lowerCAmelCase__=0 ,  lowerCAmelCase__=1 ,  lowerCAmelCase__=1 ,  **lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							__lowercase					     =				vocab_size
							__lowercase					     =				max_position_embeddings
							__lowercase					     =				d_model
							__lowercase					     =				encoder_ffn_dim
							__lowercase					     =				encoder_layers
							__lowercase					     =				encoder_attention_heads
							__lowercase					     =				decoder_ffn_dim
							__lowercase					     =				decoder_layers
							__lowercase					     =				decoder_attention_heads
							__lowercase					     =				dropout
							__lowercase					     =				attention_dropout
							__lowercase					     =				activation_dropout
							__lowercase					     =				activation_function
							__lowercase					     =				init_std
							__lowercase					     =				encoder_layerdrop
							__lowercase					     =				decoder_layerdrop
							__lowercase					     =				use_cache
							__lowercase					     =				encoder_layers
							__lowercase					     =				scale_embedding  # scale factor will be sqrt(d_model) if True
							super().__init__(
							    pad_token_id=lowerCAmelCase__ ,  eos_token_id=lowerCAmelCase__ ,  is_encoder_decoder=lowerCAmelCase__ ,  decoder_start_token_id=lowerCAmelCase__ ,  forced_eos_token_id=lowerCAmelCase__ ,  **lowerCAmelCase__ ,  )





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							return self.encoder_attention_heads





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							return self.d_model